Rejecting noise in a signal

ABSTRACT

Reducing noise in a signal is provided. A first filter includes a selectively definable passband for filtering a first signal used in determining at least one patient parameter. A characteristic analyzer detects a characteristic of a second signal and generates a variability measurement value using a series of data values within the second signal over a previously occurring window of time. A filter controller coupled to the characteristic analyzer uses the variability measurement value to define a characteristic of the passband for the first filter and selectively tunes the passband of the first filter according to the defined characteristic. Related apparatus, systems, techniques, and articles are also described.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/325,682, filed Jan. 11, 2017, which is a national stage application,filed under 35 U.S.C. § 371, of International Application No.PCT/US2015/046583, filed Aug. 24, 2015, which claims priority under 35U.S.C. § 119(e) to U.S. provisional patent application No. 62/041,313,filed Aug. 25, 2014, the entire contents of each of which are herebyexpressly incorporated by reference in its entirety.

TECHNICAL FIELD

The subject matter described herein relates to electronic devices and,more particularly, to an improved system for rejecting noise in a signalsensed by a sensor and used in determining a physiological parameter ofa patient.

BACKGROUND

The use of electronic devices to perform any number of tasks hassteadily increased over time. This is especially true in the field ofproviding healthcare to patients. In the medical field, patientmonitoring devices and/or systems are selectively coupled to a patientvia at least one sensor, which senses information from the patient andis used in deriving at least one physiological parameter associated withthe patient.

One type of patient monitoring device is a pulse oximeter. A pulseoximeter measures arterial blood oxygen saturation (SpO₂) and pulse rate(PR) using principles of light transmission and absorption and generatesa photoplethysmogram (PPG) signal. The pulse oximeter uses a sensoraffixed to one of a predetermined position of the patient. Examples ofpredetermined positions on patients to which a pulse oximeter may beaffixed includes, but is not limited to, a finger, foot, earlobe, toe,cheek, nose, nasal alar, scalp, wrist and torso. The sensor typicallycontains at least two light emitting diodes (LEDs) and a photodiodedetector. The LEDs emit light at red (˜660 nm) and infrared (˜880 nm)wavelengths, some of which is absorbed by the patient's tissues andfluids, and some of which reaches the photodiode detector. The abovedescribed wavelengths of light emitted by the LEDs is described forpurposes of example only and, in operation, the LEDs may emit light atany wavelength that falls within the red region and infrared region.More generally, it is possible the LEDs may emit light at two differentwavelengths even outside the red and infrared regions. Furthermore,multi-LED sensors may measure at additional wavelengths outside the redand infrared regions. Oxygenated and deoxygenated hemoglobin absorb redand infrared light differently. Changing blood oxygen concentrationchanges the relative absorption at the two wavelengths. The acquired redand infrared signals can then be analyzed to measure the bloodoxygenation. The greater the tissue and fluid between the emitters anddetector, the less red and infrared light that reaches the detector. Asa result, the measured PPG signals contain a constant (DC) component anda pulsatile (AC) component. The DC component results from the fixedabsorbers, including skin, muscle, fat, bone, and venous blood. The ACcomponent results from the periodic pulsations of the heart, drivingchanges in arterial blood volume. The PR can be measured from the PPG bydetecting pulse peaks, and counting their number over a fixed timeperiod (e.g., 60 s). The SpO2 can be measured by calculating the ratioof AC to DC components in both the red and infrared signals, which iscommonly referred to as the “ratio of ratios” R and is illustrated belowin Equation 1.R=(AC _(r) /DC _(r))/(AC _(ir) /DC _(ir))  (1)

The resulting value R determined in Equation 1 is used to look up theSpO₂ value in an experimentally-determined reference table as is knownin the art. Equation 1 is provided for the purposes of example only and,in operation, an oximeter may use a different method of calculatingSpO₂.

A drawback associated with PR and SpO₂ values determined by the pulseoximeter is their susceptibility to noise present in the signal beingmeasured. Types of noise that may be present in the signal beingmeasured may include any of (a) electronic noise; (b) ambient light; (c)electrocautery noise and (d) any other type of noise from any source.The following illustrates an example where the noise present iselectronic noise. However, it should not be construed to mean that thesignal includes only a single type of noise. The signal may in factinclude a plurality of different types of noise at any given timedepending on the environmental conditions surrounding the pulseoximeter.

Analog electronic components introduce noise into the measured PPGsignal. Noise corrupts the LED signal that is transmitted through thepatient's tissue. When the patient's tissue is very opaque (e.g. whenthe sensor site is a thick appendage like a neonatal foot, or when theskin has dark pigmentation) most of the LED signal is absorbed in thetissue and only a weak signal is received by the oximeter circuit. Thereceiver circuit can compensate for a weak signal by amplifying it;however, the signal and noise are amplified together. To make mattersworse, the act of amplifying the signal introduces additional noise.Indeed, most filtering and amplification operations performed in analogcircuitry introduce additional noise into the measured signal. Thisdecreases the signal-to-noise ratio (SNR). Thus, because the signal ACcomponent is typically a very small fraction of the overall measuredsignal (often on the order of 1% or less), the AC signal may easily beobscured or overcome with noise resulting in an incorrect measurement ofthe AC component. This noise makes it more difficult for digital signalprocessing to estimate the PR and SpO2 because the AC values (AC_(r) andAC_(ir)) used in Equation 1 would be less accurate. Incorrect measureslead to false alarms and contribute to the clinical problem of alarmfatigue, wherein clinicians become desensitized to overactive alarms. Itis thus highly desirable to remove noise from the measured PPG signals,to improve patient monitoring.

The noise can be decreased and the SNR can be increased by narrowing thesignal bandwidth. Some noise may be white (that is, constant across allfrequencies), such as that introduced by resistive circuit elements.Other noise may have a 1/f distribution (that is, the noise becomes lesspowerful with increasing frequency), such as that introduced by activesemiconductor circuit elements. In either case, the noise can beapproximated as white since the PPG bandwidth of interest is verynarrow, typically no more than ˜5 Hz. The power of white noise increaseswith the square root of the signal bandwidth. If the signal bandwidthcan be reduced by a factor of four, the noise will be reduced by afactor of two.

SUMMARY

In one aspect, an apparatus for reducing noise in a signal is provided.A first filter includes a selectively definable passband for filtering afirst signal used in determining at least one patient parameter. Acharacteristic analyzer detects a characteristic of a second signal andgenerates a variability measurement value using a series of data valueswithin the second signal over a previously occurring window of time. Theseries of data values within the second signal includes past values ofthe detected characteristic of the second signal over a previouslyoccurring window of time. A filter controller coupled to thecharacteristic analyzer uses the variability measurement value to definea characteristic of the passband for the first filter and selectivelytunes the passband of the first filter according to the definedcharacteristic.

In another aspect, a method of reducing noise in a signal is provided.The method includes selectively defining a passband of a first filterfor filtering a first signal used in determining at least one patientparameter and detecting, by a characteristic analyzer, a characteristicof a second signal. A variability measurement value is generated using aseries of data values within the second signal over a previouslyoccurring window of time and a filter controller uses the variabilitymeasurement value to define a characteristic of the passband for thefirst filter. The passband of the first filter is selectively tunedaccording to the defined characteristic of the passband.

One or more of the following features can be included in any feasiblecombination. For example, the characteristic analyzer can continuallygenerate the variability measurement values over successive timeintervals and the filter controller continually adjusts the passband ofthe first filter. The characteristic of the second signal detected canbe associated with the at least one patient parameter. Thecharacteristic of the passband can include at least one of (a) a centerfrequency for the passband; (b) a width of a frequency envelope for thepassband; (c) lower and upper cutoff frequencies for the passband; and(d) a shape of the frequency envelope for the passband. Thecharacteristic of the passband can include data representing a guardband that expands the width of the passband.

A first sensor can sense the first signal from a patient. A secondsensor can sense the second signal from the patient. The second sensorcan be independent from the first sensor. A parameter processor can becoupled to each of the characteristic analyzer and the first filter. Theparameter processor can use the filtered first signal to determine theat least one patient parameter. The parameter processor can compare theat least one patient parameter with the characteristic of the secondsignal to determine a signal quality index (SQI) measurement. Thecharacteristic analyzer can selectively modify at least one parameter ofthe characteristic analyzer used in determining the variabilitymeasurement value when the comparison indicates the SQI measurement isbelow a threshold value. The first signal can be a photoplethysmogram(PPG) signal. The at least one patient parameter can include at leastone of a pulse rate (PR) of a patient and a blood oxygen saturationlevel (SpO2) of a patient. The second signal can include anelectrocardiogram (ECG) signal.

The first filter can filter a signal measured from light at a firstwavelength. A second filter can filter a signal measured from light at asecond wavelength. The second wavelength can be greater than the firstwavelength. The passband for each of the first filter and the secondfilter can be adjusted using the filter parameter generated by thefilter controller. The variability measurement of the second signal canrepresent a heart rate variability (HRV) over a predetermined periodusing a heart rate variability measurement technique. The characteristicanalyzer can determine the heart rate variability (HRV) using at leastone of (a) a time domain measurement technique; (b) a frequency domainmeasurement technique; (c) a joint time-frequency domain measurementtechnique; (d) a nonlinear dynamic measurement technique; and (e) anyother type of HRV measurement technique.

The characteristic of the second signal can be a heart rate (HR) derivedfrom the ECG signal. The variability measurement value can represent aheart rate variability (HRV) over a predetermined prior period. Theparameter processor can determine signal quality index (SQI) bycalculating a pulse rate variability (PRV) over the predetermined periodwindow and comparing the pulse rate variability to the heart ratevariability. The passband of the first filter represents a frequency ofheart beats.

The variability measurement values can be continually generated oversuccessive time intervals. The passband of the first filter can becontinually adjusted, by the filter controller. The characteristic ofthe second signal detected can be associated with the at least onepatient parameter. The activity of defining the characteristic of thepassband can include at least one of (a) defining a center frequency forthe passband; (b) defining a width of a frequency envelope for thepassband; (c) defining lower and upper cutoff frequencies for thepassband; and (d) defining a shape of the frequency envelope for thepassband. The activity of defining the characteristic of the passbandcan include generating data representing a guard band that expands thewidth of the passband and expanding the width of the passband of thefirst filter using the data representing the guard band.

The first signal from a patient can be sensed via a first sensor. Thesecond signal from the patient can be sensed via a second sensor. Thesecond sensor can be independent from the first sensor. The filteredfirst signal can be used, by a parameter processor, to determine the atleast one patient parameter. The at least one patient parameter can becompared by the parameter processor with the characteristic of thesecond signal to determine a signal quality index (SQI) measurement. Atleast one parameter of the characteristic analyzer used in determiningthe variability measurement value can be selectively modified when thecomparison indicates the SQI measurement is below a threshold value.

A signal measured from light at a first wavelength can be filtered viathe first filter. A signal measured from light at a second wavelengthcan be filtered via a second filter. The second wavelength can begreater than the first wavelength. The passband can be adjusted for eachof the first filter and the second filter using the at least one filterparameter generated by the filter controller. The variabilitymeasurement of the second signal can represent a heart rate variability(HRV) over a predetermined period. The heart rate variability (HRV) canbe determined by the characteristic analyzer using at least one of (a) atime domain measurement technique; (b) a frequency domain measurementtechnique; (c) a joint time-frequency domain measurement technique; (d)a nonlinear dynamic measurement technique; and (e) any other type of HRVmeasurement technique.

A signal quality index (SQI) can be determined by the parameterprocessor. A pulse rate variability (PRV) over the predetermined periodwindow can be calculated and the pulse rate variability can be comparedto the heart rate variability. The passband of the first filter canrepresent a frequency of heart beats.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, causes at least one data processor to performoperations herein. Similarly, computer systems are also described thatmay include one or more data processors and memory coupled to the one ormore data processors. The memory may temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g. the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a device for reducing noise in a signal;

FIG. 2 is a block diagram of device for reducing noise in a signal;

FIG. 3 is a flow diagram detailing an algorithm for reducing noise in asignal;

FIG. 4 is a graph depicting an exemplary signal in which noise may bereduced using the algorithm in FIG. 3 ;

FIG. 5 is a graph depicting instantaneous heart rate plotted against anaverage heart rate over a period of time;

FIG. 6 is a graph depicting an exemplary signal having a frequencyenvelope used in minimizing noise according to the prior art;

FIG. 7 is a graph depicting an exemplary signal having a frequencyenvelope used in minimizing noise;

FIG. 8 is a graph depicting a more detailed view of a portion the signalin FIG. 7 showing the frequency envelope used in minimizing noise;

FIG. 9 is a surface map illustrating dynamic range of a pulse oximeterwhen the PPG signal is filtered with a 5 Hz band pass filter;

FIG. 10 is a surface map illustrating dynamic range of the pulseoximeter when the PPG signal is band-pass filtered with the BPF centerfrequency set to the averaged HR with a predetermined guard band of ±0.5Hz; and

FIG. 11 is a surface map illustrating dynamic range of the pulseoximeter when the PPG signal is band-pass filtered using measures of HRVwith a guard band of ±0.1 Hz.

DETAILED DESCRIPTION

Some aspects of the current subject matter describe a filtering approachto remove noise and improve the SNR. A system according to some aspectsof the current subject matter addresses the deficiencies associated withderiving patient parameters from a signal sensed by a sensor.

Reducing electronic noise in a signal sensed by a patient connectedsensor is provided. In some implementations, an apparatus advantageouslysenses a first signal from a patient and suppresses noise introducedinto the first signal using a characteristic derived from a secondsignal, which is correlated in frequency to the first signal, sensed bya second patient connected sensor to dynamically tune (e.g., set)parameters of a filter that filters the first sensed signal. The noiseintroduced in the first signal may include at least one of (a)electronic noise; (b) ambient light; (c) electrocautery noise; and (d)any other type of noise from any source. Additionally, as used herein,dynamically tuning a filter includes the activity of dynamically and inreal-time using a characteristic to modify at least one characteristicof the filter to define a range of frequencies to at least one of (a) bepassed therethrough; and (b) be excluded thereby. The characteristicused to dynamically tune a filter for filtering the first sensed signalmay be based on a variability parameter derived from a series ofinstantaneous data values from the second signal and is associated witha portion of the first signal that is of interest for determining atleast one type of patient parameter.

The filter may include a definable passband which refers to a range offrequencies of a particular signal that are allowed to pass through thefilter for further processing thereof. Moreover, the passband may betuned using the characteristic derived from the second signal. Thedynamic tuning of the filter occurs by determining a frequency envelopeassociated with the passband and which is based on the value of thecharacteristic and controlling the filter to allow signals havingfrequency content within the envelope to pass therethrough whileexcluding signals having frequency content outside of the envelope (e.g.higher or lower frequencies). Because the characteristic is associatedwith the interested portion of the first signal, the determined envelopeis narrow enough to maximize the amount of noise suppressed whileminimizing the chance that a signal portion of interest will beexcluded. In one implementation, dynamically tuning the filter mayinclude defining a characteristic of a passband of the filter by settingat least one of (a) a center frequency for the passband; (b) a width ofthe passband; and (c) a shape of the passband.

In another implementation, dynamically tuning the filter may include atleast one of (a) adjusting a center frequency of the filter; (b)adjusting an upper cutoff frequency for the filter; and (c) adjusting alower cutoff frequency of the filter. Moreover, by dynamically tuningthe filter as discussed above, the apparatus may automatically modifythe parameters used to tune the filter on a period-by-period basis. Thisadvantageously enables tuning of the filter to occur on a rolling basisthereby taking advantage of any changes in the characteristic derivedfrom the second signal to improve the suppression of any noise in thefirst signal. By dynamically tuning a filter using the characteristic,the apparatus advantageously achieves a balance between maximizing thepassage of the valid component of the first sensed signal and minimizingthe passage of in-band noise.

An exemplary apparatus 100 for reducing noise in a signal is provided inFIG. 1 . The exemplary apparatus 100 of FIG. 1 may be a patientmonitoring device 102 having a first type of patient connected sensor120 that senses a first type of signal from the patient. A second typeof patient connected sensor 130 that senses a second type of signal fromthe patient is also connected to the patient monitoring device 102.

The patient monitoring device 102 includes a parameter processor 104 forselectively processing the first type of signal and the second type ofsignal to determine a plurality of different patient parameters. Theparameter processor 104 represents circuitry that is specificallyconditioned to execute at least one type of patient parameter processingalgorithm that uses data contained in the first type of signal andsecond type of signal to derive respective types of patient parameterdata that may be output to a clinician via an output processor 106. Theoutput processor 106 may receive and format for display the patientparameter data determined by the parameter processor 104. In anotherimplementation, the output processor 106 may comprise communicationfunctionality and generate a message including the patient parameterdata. The output processor 106 selectively connects to a communicationnetwork to communicate the message to a remote system (e.g. a healthcareinformation system, a central monitoring station, etc.). Oncecommunicated, the patient parameter data in the message may be used by aclinician to support the delivery of healthcare to the patient.

To derive the patient parameter data used in supporting the delivery ofhealthcare to a patient, the patient monitoring device 102 includes aplurality of components that process and/or modify the signals sensed bythe first type of sensor 120 and second type of sensor 130. The patientmonitoring device 102 includes at least one filter 108 that filters thefirst type of signal to exclude all components outside of apredetermined frequency band, allowing only an interested portion of thefirst type of signal to be provided to the parameter processor 104 foruse in determining the patient parameter data. Because the apparatusminimizes noise contained in the first type of signal, it is desirableto determine a frequency envelope that is narrow enough to exclude asmuch of the noise as possible but wide enough to ensure that thefiltered signal being provided to the parameter processor 104 includesall information that is relevant for determining the patient parameterdata. The determination of the frequency envelope is advantageouslyaccomplished using a characteristic derived from the second type ofsignal, which is correlated in frequency to the first signal, sensed bythe second sensor 130.

The patient monitoring device 102 includes a characteristic analyzer 110that derives, from the second type of signal, a characteristic known tobe associated with the interested portion of the first type of signal.The characteristic is a variability measurement representing thevariability of a type of data over a predetermined period prior to thedetermination thereof by the characteristic analyzer 110. In oneimplementation, the predetermined period may represent a number ofindividual data values. In another implementation, the predeterminedperiod may represent a period of time. The characteristic analyzer 110generates a variability measurement that is provided to a filtercontroller 112. The filter controller 112 translates the variabilitymeasurement into a filter control parameter used to control the at leastone filter 108 by establishing a bandwidth envelope defining thepassband for the filter 108. The bandwidth envelope includes an upperbound and lower bound enabling signals having a frequency between theupper and lower bounds to pass therethrough. In one implementation, thebandwidth envelope established by the filter control parameter may alsoinclude a guard band. The guard band represents an increase in frequencyrange above the upper bound and below the lower bound of the envelope tominimize the chance of excluding a relevant signal. In anotherimplementation, the guard band may increase the frequency range abovethe upper bound. Alternatively, the guard band may decrease thefrequency range below the lower bound. To generate the guard band, thefilter controller 112 automatically modifies the filter controlparameter determined based on the variability measure to increase thewidth of the envelope to compensate for any errors in the variabilitymeasure determined by the characteristic analyzer. Thus, in real time,at each interval, the characteristic analyzer 110 may determine thevariability measure that is used to continually determine and modify, asneeded, the filter parameter used to define the frequency envelope ofthe filter 108. Thus, at each interval at which the first signal issensed by the first sensor 120, the filter 108 is automaticallyconfigured to include a passband to catch signals that are relevant indetermining the patient parameter data. The variability measure used inconfiguring the frequency envelope is associated with the patientparameter data being determined by the parameter processor 104.Moreover, as this configuration occurs in real time and at eachmeasurement interval (e.g. rolling configuration), the patientmonitoring device 102 advantageously suppresses the noise contained inthe first type of signal by excluding those signals which have afrequency outside the frequency envelope.

While the exemplary apparatus 100 of FIG. 1 is described as a singlepatient monitoring device, one skilled in the art will understand thatcomponents described herein may be implemented in multiple devices thatare able to communicate with one another via a communication network.Alternatively, these two devices may be connected to a single dockingstation and the communication therebetween may be facilitated by thedocking station.

Additionally, the description of the patient monitoring device includinga single filter 108 is shown for purposes of example only and isdescribed to illustrate the principles of the current subject matter.However, the patient monitoring device 102 may have a plurality offilters that may be used to filter different portions of the first typeof signal. For example, if the first sensor 120 senses multiple signals,the patient monitoring device 102 may include a number of filters 108equal to the number of signals sensed by the first sensor 120. Each ofthese filters may be selectively controlled using the filter parametergenerated by the filter controller 112 based on the variabilitymeasurement.

An exemplary patient monitoring device described in FIG. 1 isillustrated in greater detail in FIG. 2 . FIG. 2 depicts a patientmonitoring device 200 wherein the first sensor is a pulse oximeter 220that senses a first signal representing a photoplethysmogram (PPG). ThePPG signal includes a signal measured from light sensed at a firstwavelength (e.g. red light at substantially 660 nm) and a signalmeasured from light sensed at a second wavelength (e.g. infrared lightat substantially 880 nm). The patient monitoring device 200 includes afirst set of filters 207 a for filtering the signal from light of thefirst wavelength and a second set of filters 207 b for filtering thesignal from light of the second wavelength. The first and second set offilters 207 a, 207 b each include a bandpass filter (BPF) 208 a and 208b, respectively, and a low pass filter (LPF) 209 a and 209 b,respectively. The portion of the signal filtered by the bandpass filters208 a, 208 b produce an AC component. Therefore, as there are twodifferent signals/wavelengths sensed by the pulse oximeter 220, bandpassfilter 208 a generates an AC component associated with light of thefirst wavelength (AC_(r)) and bandpass filter 208 b generates an ACcomponent associated with light of the second wavelength (AC_(ir)).Signals from light of the first wavelength and second wavelength arealso filtered by the low pass filters 209 a, 209 b to generate DCcomponents. The output of the low pass filter 209 a is a DC componentassociated with the signal from light of the first wavelength (DC_(r))and the output of the low pass filter 209 b is a DC component associatedwith the signal from light of the second wavelength (DC_(ir)). Thevalues of AC_(r), AC_(ir), DC_(r) and DC_(ir) are provided to theparameter processor 204 which determines patient parameter datarepresenting an SpO₂ value and a PR value for the patient at a giventime. An exemplary plot 400 of the signal from light of the firstwavelength and the signal from light of the second wavelength is shownin FIG. 4 . In FIG. 4 , the PPG signal is measured from the foot of aneonatal patient. The signal is weak and corrupted by strong electronicnoise (i.e. it has a low SNR). It is difficult to accurately measure PRand SpO₂ during such periods of low SNR without advanced filteringtechniques.

It is highly desirable to control the filter parameters used in defininga frequency envelope of bandpass filters 208 a and 208 b for filteringthe signals from light of the first and second wavelengths to improvethe accuracy of the determined PR and SpO₂. To control the filterparameters, the patient monitoring device 200 also senses a secondsignal representing an electrocardiogram (ECG) signal via a plurality ofpatient-connected electrodes 230. The second sensor (i.e., the pluralityof patient-connected electrodes) 230 sensing a second signalrepresenting an ECG signal is described for purposes of example only andthe second signal may include any type of signal that can be correlatedin frequency to the first signal. Examples of other types of secondsignals include any of (a) invasive arterial blood pressure (IBP or ABP)signals; (b) ballistocardiogram (BCG) signals; (c) a second PPG signalderived from a pulse oximeter positioned at a different location thanthe pulse oximeter that generates the first signal; and (d) any othertype of signal that can be correlated in frequency to the first signal.It is further desirable that the second signal is derived independentlyfrom the first signal to further improve the reliability. The electricalimpulses sensed by the ECG electrodes are provided to the characteristicanalyzer 210. The characteristic analyzer 210 includes a beat detector211 that analyzes the electrophysiological data sensed by the electrodesand determines an instantaneous heart rate (HR) series. Thecharacteristic analyzer also includes a heart rate variability (HRV)analyzer 213 that uses the instantaneous HR series data over animmediately preceding period to determine an HRV measure representingthe variability of the patient's HR over the prior period. Theimmediately preceding period may be referred to as the analysis windowor variability window. The HRV measurement value output by the HRVanalyzer 213 is provided to the filter controller 212 which calculates afilter parameter using the HRV measure. The filter parameter controlsrepresent a frequency envelope and are used to tune the bandpass filters208 a and 208 b to define the passband of each bandpass filter 208 a and208 b. This advantageously enables the device to dynamically tune thefilter(s) in real-time based on a characteristic known to be correlatedwith the portion of the first signal that is of interest and used todetermine both the PR and the SpO₂ of the patient.

By using the HRV measure derived by the HRV analyzer 213 to tune thepassband of the BPF, a filtering balance between maximizing the validpulse frequency content and minimizing the in-band noise may beachieved. Moreover, it is understood that the relevant information inthe PPG component (signals from red and infrared light) that is used bythe parameter processor 204 in determining a PR value and a SpO₂ valueexists at or near the HR frequency. Thus, the characteristic of thesecond signal (HR and HRV) is associated with the portion of the firstsignal (PPG) that is of interest when determining patient parameterdata.

HRV is a term to describe variations of both instantaneous heart rateand RR intervals. The HRV is a reflection of the subject's autonomicstate. There are many different causes of variation in a patient's HRover time. One cause may result from a patient changing from a state ofhealth/relaxation to one of disease/stress, thereby changing the HRpattern. Additionally, the HR of a patient may fluctuate in a highfrequency band (HF, 0.15-0.4 Hz). This fluctuation, called respiratorysinus arrhythmia (RSA), is a healthy heart arrhythmia. Anotherphenomenon known as baroreflex may cause the HR to fluctuate in a lowfrequency band (LF, 0.04-0.15 Hz). One example of this is a Meyer wavewhich causes low frequency (˜0.1 Hz) fluctuations and is especiallycommon among pediatric subjects under high stress. Finally, long-termcycles such as the circadian rhythm may cause HR fluctuations in a verylow frequency band (VLF, <0.04 Hz). Other terms that may be used todescribe oscillation in consecutive cardiac cycles includes, forexample, cycle length variability, heart period variability, heartperiod variability, RR variability, and RR interval tachogram.

To compensate for these and other phenomena that cause variation in theHR of the patient, the beat detector 211 generates a series of datavalues representing instantaneous HR data. The instantaneous HR datavalues represent normal-to-normal (NN) heartbeat intervals, which arethose intervals between adjacent heartbeats resulting from sinus nodedepolarization. NN intervals reflect the autonomic state. Theinstantaneous HR data values may also include abnormal beats that resultfrom other causes (e.g. premature ventricular beats, premature atrialbeats, nodal premature beats, bundle branch block beats,supraventricular premature or ectopic beats, R-on-T prematureventricular contractions, fusion of ventricular and normal beats, atrialescape beats, nodal escape beats, supraventricular escape beats,ventricular escape beats, paced beats, fusion of paced and normal beats,etc.). Only the NN heartbeat intervals reflect the autonomic state, andother beats are typically excluded from HRV analysis. However, thepresent system is concerned with measuring the frequency of heart beatpulses. If an abnormal (non-autonomic) heartbeat produces a pulse, thenit will be manifested in the PPG signal and should be used in the HRVcalculation. Thus, the beat detector 211 advantageously characterizesall beats regardless of their origin. The parameter processor 204configures a window used by the HRV analyzer 213. The window representsthe predetermined period of instantaneous HR data to be analyzed by theHRV analyzer 213. In one implementation, the predetermined period may beset equal to 10 seconds because the HR can vary widely in a typical ˜10s window based on the presence of multiple cycles of respiration-drivenfluctuations (RSA). For example, in an adult patient breathing slowly at12 breaths per minute, two RSA cycles would occur in a 10 second windowwhereas in a neonatal subject breathing quickly at 30 breaths perminute, the window would contain 5 RSA cycles. The window may alsocontain approximately one cycle of lower frequency fluctuations. This isparticularly true in pediatric subjects, who are prone to exhibitingMeyer waves at ˜0.1 Hz. Thus, in contrast to simply averaging theinstantaneous HR data over the window and using the average value, theHRV analyzer 213 advantageously maintains the information associatedwith instantaneous fluctuations of the patient HR allowing thosevariations to better tailor the bandwidth envelope of bandpass filters208 a and 208 b. The difference between a typical 10 s averaged HRseries and its instantaneous HR counterpart is illustrated in plot 500of FIG. 5 . Therein, it is readily apparent that using an average HRillustrated by line 502 as compared to the instantaneous HR counterpartillustrated in line 504, would require the bandwidth envelope of thebandpass filters 208 a and 208 b to be either unreasonably large(conservative) thereby including undesirable noise, or unreasonablynarrow (aggressive) thereby excluding pulsatile information that isrelevant in determining the PR and SpO₂ value for the patient. Acritical concern is that the HRV is not constant in time or acrosssubjects and the frequencies and amplitudes can vary widely. Therefore,without factoring the HRV into defining the boundaries of the bandwidthenvelope for the bandpass filter 208 a and 208 b, there is an increasedrisk of either passing undesired in-band noise or rejecting validpulsatile information from the PPG signal resulting in less accurate PRand SpO₂ values generated by the parameter processor 204.

The HRV analyzer 213 advantageously generates an HRV measure accordingto at least one type of HRV measurement technique. HRV measurementtechniques able to be implemented by the HRV analyzer 213 include atleast one of (a) a time domain measurement technique; (b) a frequencydomain measurement technique; (c) a joint time-frequency domainmeasurement technique; (d) a nonlinear dynamic measurement technique;and (e) any other type of HRV measurement technique. The listedtechniques are not meant to be construed as limiting and any algorithmictechnique able to measure HRV may be employed by the HRV analyzer. Asused herein, the term technique represents a type of algorithmexecutable by the HRV analyzer 213 circuitry.

In one implementation, the HRV analyzer 213 may determine the HRVmeasurement value by analyzing the instantaneous HR series data in thetime domain. Generally, the time domain measurement techniques apply astatistical analysis of the NN intervals or difference between NNintervals. One example of a statistical analysis applied in the timedomain is the standard deviation of NNs (SDNN) which estimates theoverall variability of the HR. The overall variability of the HR islinked to autonomic state, with increased variability associated withstates of health/relaxation, and decreased variability associated withstates of disease/stress. SDNN is typically used in analysis windows 5minutes long; however, the method continues to function even when thewindow is decreased to 1 min and possibly even shorter, since thepresent HRV analysis is not being used to measure autonomic state of thepatient but rather the variation of frequencies associated with all HRpulses within the window. In some example implementations, the HRVanalyzer 213 receives the instantaneous HR series from the beat detector211 and computes the HRV as the standard deviation of a predeterminednumber of samples (or length of time). Another example of a statisticalanalysis applied to generate an HRV measurement value is the root meansquare of successive differences between NNs (RMSSD). RMSSD estimatesthe short term high frequency variation in the HR. Though widely used,RMSSD can be combined with other algorithms for the purposes of thecurrent subject matter. It can be desirable for the HRV analyzer 213 tocapture the low frequency HR variations (in addition to the highfrequency variations from RMSSD), as all HRV frequencies can beimportant in some implementations. In some example implementations, theHRV analyzer 213 receives the instantaneous HR series from the beatdetector 211 and computes the HRV as the root mean square of thebeat-to-beat changes in the HR series over a predetermined number ofsamples (or length of time). The root mean square for n samples may becomputed according to:

$x_{rms} = \sqrt{\frac{1}{n}\left( {x_{1}^{2} + x_{2}^{2} + \ldots + x_{n}^{2}} \right)}$

where x_(i) represent the difference between two successive HR samples.

Implementations of SDNN and RMSSD for the purposes of the currentsubject matter would be expanded to additionally process non-NNintervals, since some implementations of the present subject matter canmeasure the variability of the HR rather than the patient's autonomicstate. A further example of a time domain measurement technique involvespoint process modeling. Point process modeling models the HR samples asrandom variables drawn from a statistical point process. The randomvariables have been modeled using the Inverse Gaussian distribution,whose parameters are estimated from the recent history of beat times.The parameters are continually updated with new estimations each time anew beat occurs. In some example implementations, the HRV analyzer 213receives the instantaneous HR series from the beat detector 211 andcomputes the inverse Gaussian distribution parameters (e.g., mean andshape). The HRV can be determined from the inverse Gaussiandistribution, which can be described by:

${pdf} = {\left\lbrack \frac{\lambda}{2\pi\; x^{3}} \right\rbrack^{\frac{1}{2}}{\exp\left( \frac{- {\lambda\left( {x - \mu} \right)}^{2}}{2\mu^{2}x} \right)}}$

where μ represents the mean, and λ represents the shape parameter. Themaximum likelihood estimate of the standard deviation may be used as theHRV.

In some implementations, the point process modeling can include ahistory-dependent inverse Gaussian (HDIG) point process. With HDIG, itis assumed that given any R-wave event λ_(k), the waiting time until thenext R-wave event (e.g., the length of the next R-R interval) obeys anHDIG probability density described by:

${pdf} = {\left\lbrack \frac{\theta_{p + 1}}{2{\pi\left( {t - u_{k}} \right)}^{3}} \right\rbrack^{\frac{1}{2}}{\exp\left( \frac{- {\theta_{p + 1}\left( {t - u_{k} - {\mu\left( {H_{k},\theta} \right)}} \right)}^{2}}{2{\mu\left( {H_{u_{k}},\theta} \right)}^{2}\left( {t - u_{k}} \right)} \right)}}$where H_(k)=(u_(k), w_(k), w_(k−1) . . . , w_(k−p+1)),w_(k)=u_(k)−u_(k−1) is the k^(th) R-R interval, μ(H_(k),θ)=θ₀+Σ_(j=1)^(p)θ_(k)w_(k−j+1)>0 is the mean, θ_(p+1)>0 is the scale parameter, andθ=(θ₀, θ₁, . . . , θ_(p+1)) is a vector of time-varying modelparameters. HRV may be one or more of the maximum likelihood estimate ofthe standard deviation of the R-R interval or the heart rate. Given themaximum likelihood estimate of θ_(t), the maximum likelihood estimate ofthe standard deviation of the R-R interval and the heart rate is givenrespectively by:

${\hat{\sigma}}_{{RR}_{t}} = \left\lbrack {{\mu\left( {H_{u_{k}},{\hat{\theta}}_{t}} \right)}^{3}{\hat{\theta}}_{{p + 1},t}^{- 1}} \right\rbrack^{\frac{1}{2}}$${\hat{\sigma}}_{{HR}_{t}} = \left\lbrack \frac{{2{\mu_{t}^{*}\left( {H_{t},{\hat{\theta}}_{t}} \right)}} + {\hat{\theta}}_{{p + 1},t}^{*}}{{\mu_{t}^{*}\left( {H_{t},{\hat{\theta}}_{t}} \right)}{\hat{\theta}}_{{p + 1},t}^{*2}} \right\rbrack^{\frac{1}{2}}$

The point process modeling method advantageously can convert a discrete,unevenly sampled signal (the HR series) into a continuous time signalwith arbitrary resolution enabling estimation of HRV parameters betweenbeats. Moreover, the point process modeling technique advantageously canpredict the time of the next heartbeat, which allows the HRV analyzer213 and filter controller 212 to better tune BPF parameters in advanceof upcoming pulses.

In another implementation, the HRV analyzer 213 may determine the HRVmeasurement value by analyzing the instantaneous HR series data in thefrequency domain. Performing a frequency domain analysis advantageouslyreveals an underlying structure to the HR series. The HRV analyzer 213performs a Fourier transform (FT), which transforms the HR series fromthe time domain into the frequency domain, and the resulting powerspectrum can be divided into different frequency bands. The power inthese frequency bands has been shown to reflect the autonomic state. Oneexample of a frequency domain measurement technique includes analyzingthe power in the low frequency (LF, 0.04-0.15 Hz) and high frequency(HF, 0.15-0.4 Hz) bands, measuring their raw power (LF or HF power),normalized power (LFnu or HFnu), or power ratio (LF/HF ratio). Frequencydomain measurements may be useful for HR prediction. The overall poweris approximately equivalent to the SDNN, and may be used to estimate theoverall HR variance. The power and phase of various frequencies may beused to predict the HR trajectory (e.g. rising or falling, based on itssinusoidal phase), so that the BPF can be tuned in advance of upcomingpulses. In performing a frequency domain measurement technique, the HRVanalyzer 213 resamples the HR series derived by the beat detector 211onto an evenly spaced grid. Once resampled, the HRV analyzer executes afast Fourier transform (FFT) algorithm to transform the resampled datainto the frequency domain, to determine the variability of the HR overthe window period. Typically, frequency domain measurement techniquesrequire relatively long data windows and assume a stationary timeseries. This is because the FT basis function, the complex exponential,is highly localized in frequency but poorly localized in time. Accuracytherefore degrades as the window length decreases, so longer analysiswindows are desirable.

In a further implementation, the HRV analyzer 213 may determine the HRVmeasurement value by analyzing the instantaneous HR series data using ajoint time-frequency (JTF) domain measurement technique. Using a JTFtechnique is advantageous because it is designed to operate using ananalysis window that is shorter than those used with frequency domainmeasurement techniques. The frequency domain techniques assume that theHR signal is stationary over the analysis window. However, in reality itis more likely that that HR signal is highly nonstationary. Thus, byusing shorter analysis windows a JTF measurement technique can helprelieve the stationarity assumption, as the HR series is more likely toapproach stationarity as the window shortens. JTF techniques achievethis by providing tunable localization in both time and frequency,adjusting their basis functions to shift energy between the two domains.In so doing, they produce power spectra nearly as precise as purefrequency domain techniques, but with a vastly improved timelocalization (and thus shorter analysis windows). JTF measurementtechniques advantageously respond faster to changes in HRV. Examples ofJTF measurement techniques to determine HRV measurement values include,but are not limited to (a) a wavelet transform (WT) which producesresults comparable to frequency domain measurement techniques but usestunable window lengths that can be as short as two samples long; and (b)Wigner-Ville and/or Exponential distributions (WVD & ED) which treat theHR as a stochastic process, and calculate its changing power spectraldensity over time. In performing a JTF measurement technique, the HRVanalyzer resamples the HR signal onto an even grid and then calculatesthe power in different frequency bands. As before, these can be used tohelp predict the frequency of upcoming pulses using a plurality ofsequential filtering operations to the HR signal to determine the HRVmeasurement which is used by the filter controller 212 to tune the BPFin advance of upcoming pulses.

In another implementation, the HRV analyzer 213 may determine the HRVmeasurement value by analyzing the instantaneous HR series data using anonlinear dynamic measurement technique which detects chaos in the HRseries. The nonlinear dynamics of the HR have been shown to changebetween states of health/relaxation and disease/stress. A chaotic,unpredictable HR has been found to be a marker of good health. Examplesof nonlinear dynamic measurement techniques include but are not limitedto (a) approximate entropy (ApEn) or sample entropy, which estimate theentropy of the HR series (analogous to information content, statisticalsurprisal, or unpredictability); (b) Finite time Lyapunov exponents(FTLE), which quantifies chaos from the HR's phase portrait (a graphicalmap of the changing system states over time); and (c) Detrendedfluctuation analysis (DFA), which measures fractal scaling exponents inthe HR series. By using nonlinear dynamic measurement techniques, theHRV analyzer may provide a measure of the HR's predictability, allowingfor the derivation of a confidence index for use in predicting futurepulse times. This confidence index can be output by the HRV analyzer 213and used by the filter controller 212 to tune the BPF's passband (andincluded guard band, if applicable). For example, in periods where theHR is less predictable (as evidenced by e.g. increased ApEn), the guardband can be widened to ensure that the filter does not inadvertentlyreject a future pulse that falls outside the expected frequency range.

Additionally the window used by the HRV analyzer 213 may be selectivelymodified using a feedback control signal. When analyzing the HRV, theHRV analyzer 213 may selectively control the length of the windowrepresenting the amount of HR history that will be included in theanalysis window. The HRV analyzer may store a history of HRV measurementvalues which may be compared to one another to determine a changepattern associated with multiple HRV measurement values. If the storedHRV measurement values are determined to be consistent over apredetermined period of time, the HRV analyzer may automatically shortenthe length of the window which would advantageously capture shorter termchanges in HRV and also save processing time. Conversely, if the HRVmeasurement values are changing rapidly, the HRV analyzer mayautomatically increase the length of the window thereby stabilizing themeasurement value provided to the filter controller 212 and used to tunethe bandpass filters 208 a and 208 b. Consistency may be determined indifferent ways. When using simple statistical techniques (e.g. SDNN,RMSSD), the analysis window length may be set proportional to the rateof change of overall HRV. When using frequency domain or JTF techniques,the analysis window length may be controlled by the relative power inthe HF, LF, and VLF frequency bands. For example, if the HR is found tobe varying mostly in rapid cycles (strong HF band power, but weak LF andVLF band power), then the HRV analyzer can operate on shorter analysiswindows but still capture most of the salient information. Under theseconditions the HRV analyzer may automatically shorten the length of theanalysis window. Conversely, if the LF or VLF band power becomessignificant, then the HRV analyzer may automatically lengthen theanalysis windows to capture these slower variations. The consistency maybe determined by the rate of change of the relative power between theHF, LF, and VLF frequency bands. When using nonlinear dynamics analysistechniques, the analysis window length may be controlled by the measuredpredictability of the HR series. If the HR is found to be chaotic (usinge.g. FTLE or DFA), or is found to have a high entropy (using e.g. ApEn),then the HRV analyzer may decide that past HR samples offer littleinformation about future samples, and it may automatically shorten theanalysis window accordingly.

The HRV measurement value determined by the HRV analyzer 213 using anyof the above variability measurement techniques is provided to thefilter controller 212 which automatically translates the HRV measurementvalue into a filter control parameter for use in tuning the bandpassfilters 208 a and 208 b. In one implementation, the filter controlparameter includes a set of coefficients that are used by the filter incalculating the frequency envelope to be used by the filter for theupcoming pulsatile measurement thereby defining the passband for thefilter. The HRV measurement value is successively calculated on abeat-by-beat basis using a rolling window and the filter controller 212can similarly successively calculate filter parameters. Thus, thebandpass filters 208 a and 208 b can be continually tuned on abeat-by-beat basis thereby maximizing the amount of noise rejected whileminimizing erroneous rejection of valid pulsatile signal data from thePPG signal. Thus, the HRV measurement value can be used to tune the BPFpassband bounds dynamically in time, to capture all of the relevantpulsatile information without being overly broad. In one implementation,the filter parameter calculated by the filter controller 212 may definethe upper and lower bounds based on the maximum and minimum HR valuesobserved in a given time window (e.g. ˜10 s). In another implementation,the filter parameter calculated by the filter controller 212 may definethe upper and lower bounds based on the mean HR combined with thestandard deviation of recent HR values (SDNN). In a furtherimplementation, a combination of two or more HRV measurement techniques,as described above, may be used to define the upper and lower bounds orthe shape of the frequency envelope. A guard band may optionally beincluded to hedge against unexpectedly large HR changes, ensuring allrelevant pulsatile information passes through the bandpass filters 208 aand 208 b.

In order to ensure that all relevant pulsatile information in the PPGsignal is passed through to the parameter processor 204, the filtercontroller 212 further advantageously calculates a guard band value. Theguard band represents an increase in bandwidth applied to the upperbound and/or a decrease in bandwidth applied to the lower bound of thefrequency envelope to ensure that all relevant pulsatile information ispassed to the parameter processor 204 without arbitrarily increasing thewidth of the frequency envelope resulting in passage of PPG signalshaving noise. In one implementation, the filter controller 212calculates the frequency envelope and, based on the width of theenvelope, calculates a width of a guard band to be applied at the upperand lower bound and generates filter parameter data to tune the bandpassfilters 208 a and 208 b to have a passband with a width equal to theenvelope and the guard band. In another implementation, the width of theguard band is predetermined and automatically added to increase theupper bound of the envelope and decrease the lower band of the envelopeprior to calculation of the filter parameter used to tune the bandpassfilters 208 a and 208 b.

Feedback may be used by the filter controller 212 to update the HRprediction uncertainty because the HRV measurement value provides anestimate of the upper and lower bounds (and with it the bandwidth) ofthe HR in the near future (e.g. the next few beats). These estimateswill include the guard band representing an additional padding on theupper and lower BPF cutoff limits to hedge against uncertainty in the HRprediction. For example, the recent HRV history may suggest that the HRfalls entirely within the range of 90-110 beats/min, and we may predictthat this trend will continue in the near future. However, a slightincrease in the mean HR may lead to a brief excursion above the upperlimit, e.g. at 112 beats/min. Thus, the filter controller 212 may definethe width of the guard band at substantially 2 beats/min (or more) whichprotects against such an excursion and allows for the relevant PPG topass to the parameter processor 204 for use in determining the PR andSpO₂ value for the patient. The guard band does provide an increasedopportunity for noise to pass through the bandpass filters 208 a and 208b. Thus, the filter controller 212 may advantageously minimize the guardband while also minimizing the risk that the HR will exceed ourpredicted limits using an active feedback error signal which can helpoptimize the guard band.

The filter controller 212 may store in a memory, a recent history of HRlimit predictions, and compare them to the true HR values as they occur.The filter controller 212 calculates the difference between thepredicted HR limits and the actual HR values to generate the errorsignal which is used as a feedback control to modify the width of theguard band. If the prediction limits consistently overestimate the HRbounds, then the guard band may be reduced. Conversely, if theprediction limits consistently underestimate the HR bounds, then theguard band may be increased. By using the error signal feedback control,the filter controller 212 continually, and in real-time, adapts thewidth of the guard band to be combined with the frequency envelope whichhad been determined based on the HRV measurement value. Thisadvantageously enables the system to adapt to the changing uncertaintyin the HR predictions.

In another implementation, the system may advantageously determine asignal quality of the first signal sensed by the first sensor (e.g.PPG). In this implementation, parameter processor 204 can also estimatethe PR and SpO₂ signal quality. The pulse rate (PR) and heart rate (HR)are very similar parameters, but are derived from different sourcesignals. The HR is typically derived from an ECG, while the PR can bederived from a PPG. Their associated characteristics of pulse ratevariability (PRV) and heart rate variability (HRV) are likewise verysimilar. Noise in the PPG can affect the measures of PR and PRV, causingthem to differ from their ECG-derived counterparts of HR and HRV. Bycomparing the PR to HR, and PRV to HRV, the parameter processor 204 canestimate PPG signal quality (with PR and PRV derived pre-filtering) aswell as the effectiveness of the filtering scheme (with PR and PRVderived post-filtering). The signal quality can be represented in theform of a signal quality index (SQI) which ranges from e.g. 0 (very poorquality) to 100 (very high quality). The SQI value may be compared to athreshold SQI value and, if the SQI value is below the threshold value,the characteristic analyzer may modify at least one parameter thereofused in determining the variability measurement value. The SQI value canbe derived as e.g. a normalized distance between the PR and HR or PRVand HRV measures. A high SQI suggests an accurate measure of PR andSpO₂. The description of the SQI provided herein is described forpurposes of example only and any manner of providing an indication as tothe quality of the signal to a user may be used. For example, the SQImay be provided using a color scheme wherein a first color represents ahigh quality signal and a second color represents a low quality signal.Any indication scheme may be used to provide the SQI value to the user.

Parameter processor 204 can provide the SQI as an output to subsequentalgorithms and/or to the display for the clinician. The clinician canuse the SQI to guide him/her in making healthcare delivery decisions.For example, a noisy PPG signal may cause a sudden (false) drop in SpO₂.This may appear alarming, but if reported with a low SQI the clinicianmay decide to wait to ensure it is a true clinical event and not simplythe result of noise. Alternatively, the clinician may choose toreposition the sensor or move it to a different site entirely to improvethe SQI. Subsequent algorithms can use the SQI to guide them in furtheranalyses (e.g. trend analysis). Parameter values with low SQI may beweighted less heavily than those with high SQI.

The SQI can be used as feedback to the filter controller 212, via theHRV analyzer 213, in the form of an error signal. A low SQI (i.e. alikely inaccurate measure of PR and SpO₂) may be addressed by changingfiltering parameters and re-deriving the PR and SpO₂ values and theirassociated new SQI value. In one implementation, the monitoring devicemay optimize the accuracy of the measurements of PR and SpO₂ byiterating over successively different filtering parameters, updatedbased on the SQI error signal. Parameter processor 204 would then reportthe measured PR and SpO₂ values associated with the highest SQI. Theoptimal filtering parameters may be retained in memory and used as anoptimizer starting point in subsequent analysis windows.

An algorithm 300 for tuning the filter used to filter a first signalbased on a characteristic of a second signal is provided in the flowdiagram of FIG. 3 . The description of FIG. 3 will make reference tovarious components of FIG. 1 that are controlled by the algorithm tooperate in the manner described. It is also important to note that,although the algorithm is described as a series of linear steps, thatcertain of these steps may be performed concurrently with one another orin a different order.

In step 302, a characteristic analyzer 110 detects a characteristicassociated with a second signal from a window comprising a series ofsamples received from the second sensor 130. The series of samples inthe window represent a predetermined prior period from which thecharacteristic is determined. In step 304, the determined characteristicis provided to a filter controller 112 which automatically translatesthe detected signal characteristic into at least one filter parameterrepresenting a frequency envelope. The at least one filter parameter isused to tune a filter 108 by defining the passband of the filteraccording to the frequency envelope in step 306. In step 308, the filter108 filters a signal received from a first sensor 120 to allow signalswithin the passband to be provided to a parameter processor 104 whichdetermines at least one patient parameter data value using the filteredfirst signal. The algorithm queries, in step 310, whether any successivesamples have been sensed by the second sensor 130. If the result of thequery is negative, the algorithm awaits any further samples in step 311which further reverts back to step 310. If the result of the query instep 310 is positive, the algorithm continues at step 312. At step 312,the characteristic analyzer 110 updates the series of signals in thewindow to include a most recent data value of the second signal andexclude the earliest data value of the second signal. Once the windowhas been shifted, the algorithm reverts to step 302 and the filter canbe continually tuned in real-time to have a passband that maximizes theexclusion of noise and minimizes exclusions of signals relevant indetermining the patient parameter data by the parameter processor 204.

The advantages of using a variability characteristic of one signal totune a filter for filtering another different signal will be shown inFIGS. 6-8 . FIG. 6 depicts a plot 600 of an instantaneous HR in beatsper minute over a period of time. The plot of the instantaneous HR islabeled with reference number 602. A gray shadow surrounding line 602represents the width of the passband that had been determined using amethod different from that which is described above in FIGS. 1-5 . In anattempt to define the characteristic of the passband, a prior methodused is to determine the HR, averaged over a predetermined time period,and to set the BPF center frequency to the averaged HR. Since, in thisprior method, the variability of the HR is not used, the method adds apredetermined guard band of ±0.5 Hz (±30 beats/min) to the centerfrequency thereby defining the passband. The result is a significantincrease and decrease of beats per minute around the actualinstantaneous HR 602 at any given point. This distance is illustrated bythe line labeled 604. Thus, while the passband shown in FIG. 6 rejectsnoise of very low and high frequencies, the substantial widthrepresented by line or distance 604 allows for a broad range of in-bandnoise to remain in the filtered signal. The guard band is overlyconservative (wide). The HR is unlikely to vary over such a widefrequency range, especially in the short term (˜10 s). Thus, such abroad spectrum does not otherwise provide much benefit for derivingpatient parameter data (PR and SpO₂) because the most relevantinformation exists at or near the HR. While providing little substantialbenefit, this broad spectrum passband does allow a wide window ofopportunity for in-band noise to pass through the filter. However,simply narrowing the filter is not an effective solution because thisincreases the risk of excluding valuable signal components from thepatient parameter data calculation. If the HR strays outside thefilter's passband at any point in the averaging time window (e.g. ˜10s), the corresponding pulse will be attenuated (partially or whollyrejected) having a destructive effect and leading to incorrect patientparameter data calculations (PR and SpO₂ derivations). The result wouldessentially cause the very problem the narrowing of the passband istrying to solve.

In contrast to the passband set by the method described in FIG. 6 , theadvantages of the passband set in the plot 700 of FIG. 7 using thevariability characteristic to generate the filter parameter used to tunethe filter is readily apparent. FIG. 7 includes the same instantaneousHR 602. However, the width of the passband shown in FIG. 7 is determinedusing the variability characteristic of the second signal. Herein thesecond signal is an ECG signal and the HRV is determined using any ofthe HRV measurement techniques described above in FIG. 2 . As can beseen in FIG. 7 , the distance between the instantaneous HR and the upperand lower bound of the passband indicated by line 704 is substantiallyreduced as compared to the distance 604 in FIG. 6 . The result is asignificant improvement in excluding in-band noise but minimizing theexclusion of relevant portions of the first signal being filtered by thebandpass filter. Furthermore, the HRV measurement value is continuallyupdated to allow for real-time continual generation of filter parametersfor tuning the filter in order to modify a characteristic (e.g. widthand/or shape) of the passband. Therefore, the characteristic (e.g.width) of the passband remains sufficiently narrow to continually reducethe in-band noise.

FIG. 8 is a graph 800 showing a more detailed view of the instantaneousHR over a period of time from which an HRV measurement has beencalculated and used to determine filter parameters for configuring thepassband of a filter. More specifically, FIG. 8 represents theinstantaneous HR shown in FIG. 7 between 170 seconds and 225 secondsrepresented by the box labeled 8 in FIG. 7 . As time increases, we seethe dynamic tuning of the passband based on the HRV measurement derivedfrom instantaneous HR from a preceding window of time. Because thetuning is so fine, and the time frame is so large, the width of thepassband is virtually indistinguishable from the instantaneous HR plot.The detailed view shown in FIG. 8 shows the instantaneous HR data andcompares two points, 810 and 820, which show how different filterparameters can be used to dynamically tune the filter based on a priorperiod of instantaneous HR data. The passband is much wider at 810 thanat 820, based on the recent history of HRV. By tuning based on thevariation of the HR over the predetermined period, the filter is able tomaximize the amount of relevant signals passing therethrough whileminimizing the amount of in-band noise. The method illustrated in FIGS.7 and 8 sets the upper and lower bounds of the passband to the maximumand minimum HR values (respectively) observed in the previous 30 secondsof instantaneous HR signal. A guard band of ±1 beat per minute is addedto the passband bounds. This is a very simple example implementation ofthe current subject matter principles. More robust implementationsemploying multiple HRV analysis methods are possible.

FIGS. 9-11 are surface maps 900, 1000, and 1100 illustrating the dynamicrange (e.g., signal to noise ratio performance) of a pulse oximeter whenthe PPG signal is filtered over the full 5 Hz bandwidth (FIG. 9 ), whenthe PPG signal is filtered with the BPF center frequency set to theaveraged HR with a predetermined guard band of ±0.5 Hz (FIG. 10 ), andwhen PPG signal is band-pass filtered using measures of HRV with a guardband of ±0.1 Hz according to some implementations of the current subjectmatter. The illustrated data was obtained using a simulation of a pulseoximeter. The vertical axes represent DC through-transmission of thepatient's tissue, which is a measure of the transmissivitycharacteristics of the patient's tissue. The DC through-transmission isshown in units of virtual nanoamperes (nAv), which is defined such that1 nAv equals 1 nA of receiver input current when the light emitter(e.g., light emitting diode) is driven at 100% intensity (defined as a50 mA drive current). For example, 1000 nAv=1000 nA at 50 mA emitterdrive current, and also 1000 nAv=500 nA at 25 mA drive current.

The horizontal axes represent the AC signal modulation from thepatient's arterial blood pulsations. The AC signal modulation from thepatient's arterial blood pulsations is a percent modulation of the DCvalue of the PPG signal. In other words, the AC signal modulation is ameasure of the amount of blood in each pulse (which may vary based onphysiological conditions such as body temperature). For example, 1% ACmodulation of a 1 μAv DC level represents a 10 nAv peak-to-peak ACsignal. Thus, both DC through-transmission and AC modulation areproperties of the patient and independent of particular pulse oximetryhardware.

Intensity is a measure of signal to noise ratio (SNR) of the PPG signaland indicates the accuracy of the SpO₂ measurements. The darker portionon the left side of each of FIGS. 9, 10, and 11 represents lower SNR(denoted by 905, 1005, and 1105, respectively) in which SpO₂measurements will not meet allowed oximetry standards (e.g., accuracy towithin more than 2% of true value). The grey on the right side of eachfigure represents higher SNR (denoted by 910, 1010, and 1110,respectively) which is sufficient for accurate SpO₂ measurements (e.g.,within 1% of true value). The light portion in the middle of each figurerepresents SNR values between lower and higher SNR (denoted by 915,1015, and 1115, respectively) which meets oximetry standards but maystill be less accurate (SpO₂ accuracy ranges between 1% and 2% of thetrue value).

FIGS. 9, 10, and 11 illustrate the relative performance improvement fora pulse oximeter utilizing the current subject matter. FIG. 9 containsthe largest dark region 905, corresponding to conditions in which theSpO₂ measurements are inaccurate and FIG. 10 illustrates dark region1005 having a slight improvement over the performance of FIG. 9 . FIG.11 , which corresponds to a pulse oximeter implemented according to thecurrent subject matter, has the smallest dark region 1105. Thus, animplementation of the current subject provides improvement in SNR forpulse oximetry.

Although the current subject matter has been described in terms ofexemplary implementations, it is not limited thereto. Rather, theappended claims should be construed broadly to include other variantsand implementations of the current subject matter which may be made bythose skilled in the art without departing from the scope and range ofequivalents of the current subject matter. This disclosure is intendedto cover any adaptations or variations of the embodiments discussedherein.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive trackpads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it is used, such a phrase isintended to mean any of the listed elements or features individually orany of the recited elements or features in combination with any of theother recited elements or features. For example, the phrases “at leastone of A and B;” “one or more of A and B;” and “A and/or B” are eachintended to mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” In addition, use of the term “based on,” aboveand in the claims is intended to mean, “based at least in part on,” suchthat an unrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A patient monitoring device configured to reducenoise in a measured pulse rate signal, comprising: a pulse rate sensoroperating in a red to infrared electromagnetic spectrum configured togenerate a pulse rate signal in response to pulse rate measurements; anindependently measured heart rate sensor operating outside the red toinfrared electromagnetic spectrum configured to generate a heart ratesignal in response to heart rate measurements; a characteristic analyzerconfigured to determine a measured heart rate variability based on theheart rate measurements; a filter controller configured to dynamicallytune an adjustable filter in order to generate a reduced-noise pulserate signal based on the determined heart rate variability; a parameterprocessor providing a pulse rate output signal based on thereduced-noise pulse rate signal; and wherein the characteristic analyzeris further configured to determine a predicted heart rate variability,determine a guard band based of the measured heart rate variability andthe predicted heart rate variability, determine a frequency passband forthe pulse rate signal based on the heartrate variability and the guardband, and dynamically tune the adjustable filter based on the frequencypassband.
 2. The patient monitoring device according to claim 1, whereinthe heart rate measurements comprises electrocardiogram (ECG)measurements.
 3. The patient monitoring device according to claim 1,wherein the characteristic analyzer is further configured to continuallydetermine the measured heart rate variability and continually tune theadjustable filter.
 4. The patient monitoring device according to claim1, wherein the characteristic analyzer continually determines the heartrate variability over successive time intervals and continually adjuststhe adjustable filter.
 5. The patient monitoring device according toclaim 1, wherein the frequency passband comprises at least one of (a) acenter frequency; (b) a width of a frequency envelope; (c) lower andupper cutoff frequencies; and (d) a shape of the frequency envelope. 6.The patient monitoring device according to claim 1, wherein the pulsemeasurements are first pulse rate measurements, further configured forreceiving second pulse rate measurements in the red to infraredelectromagnetic spectrum; and generating the pulse rate signal based onthe first and second pulse rate measurements.
 7. The patient monitoringdevice according to claim 6, further configured for: computing a bloodoxygen saturation level (SpO2) based on measurements the first andsecond pulse rate measurements; and providing a blood oxygen outputbased on the blood oxygen saturation level (SpO2).
 8. The patientmonitoring device according to claim 1, further configured for:determining a signal quality index (SQI) by calculating a pulse ratevariability (PRV) over a predetermined period and comparing the pulserate variability to the measured heart rate variability; and providing asignal quality output based on the signal quality index (SQI).
 9. Thepatient monitoring device according to claim 1, a wherein the parameterprocessor is further configured to: determine a signal quality index(SQI) by calculating a pulse rate variability (PRV) and comparing thepulse rate variability to the measured heart rate variability; andprovide a signal quality output reflecting the signal quality index(SQI).
 10. A method for reducing noise in a measured pulse rate signal,comprising: receiving pulse rate measurements in a red to infraredelectromagnetic spectrum; generating a pulse rate signal based on thepulse rate measurements; receiving heart rate measurements, independentof the pulse rate measurements, outside the red to infraredelectromagnetic spectrum; generating a heart rate signal based on theheart rate measurements; determining a measured heart rate variabilitybased on the heart rate signal; determining a predicted heart ratevariability; computing a guard band based the measured heart ratevariability and the predicted heart rate variability; determining afrequency passband for the pulse rate signal based on the heart ratevariability and the guard band; dynamically tuning an adjustable filterbased on the frequency passband; determining a reduced-noise pulse ratesignal based on the pulse rate signal and the adjustable filter; andproviding a pulse rate output reflecting the reduced-noise pulse ratesignal.
 11. The method of claim 10, wherein the heart rate measurementscomprises electrocardiogram (ECG) measurements.
 12. The method of claim10, further comprising continually determining the measured heart ratevariability and continually tuning the adjustable filter.
 13. The methodof claim 10, wherein defining the frequency passband comprisesdetermining at least one of (a) a center frequency; (b) a width of afrequency envelope; (c) lower and upper cutoff frequencies; and (d) ashape of the frequency envelope.
 14. The method of claim 10, furthercomprising dynamically tuning the adjustable filter using at least oneof (a) a time domain measurement technique; (b) a frequency domainmeasurement technique; (c) a joint time-frequency domain measurementtechnique; and (d) a nonlinear dynamic measurement technique.
 15. Themethod of claim 10, wherein the pulse rate measurements are first pulserate measurements, further comprising: receiving second pulse ratemeasurements in the red to infrared electromagnetic spectrum; andgenerating the pulse rate signal based on the first and second pulserate measurements.
 16. The method of claim 15, further comprising:computing a blood oxygen saturation level (SpO2) based on measurementsthe first and second pulse rate measurements; and providing a bloodoxygen output based on the blood oxygen saturation level (SpO2).
 17. Themethod of claim 10, further comprising: determining a signal qualityindex (SQI) by calculating a pulse rate variability (PRV) over apredetermined period and comparing the pulse rate variability to themeasured heart rate variability; and providing a signal quality outputbased on the signal quality index (SQI).